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  Incremental Sparsification for Real-time Online Model Learning

Nguyen-Tuong, D., & Peters, J. (2010). Incremental Sparsification for Real-time Online Model Learning. Cambridge, MA, USA: JMLR.

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Datensatz-Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C042-2 Versions-Permalink: https://hdl.handle.net/21.11116/0000-0002-81F1-B
Genre: Konferenzbeitrag

Externe Referenzen

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externe Referenz:
http://proceedings.mlr.press/v9/nguyen_tuong10a/nguyen_tuong10a.pdf (Verlagsversion)
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Urheber

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 Urheber:
Nguyen-Tuong, D1, 2, Autor           
Peters, J1, 2, Autor           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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Schlagwörter: -
 Zusammenfassung: Online model learning in real-time is required by many applications such as in robot tracking control. It poses a difficult problem, as
fast and incremental online regression with
large data sets is the essential component
which cannot be achieved by straightforward
usage of off-the-shelf machine learning methods
(such as Gaussian process regression or
support vector regression). In this paper,
we propose a framework for online, incremental
sparsification with a fixed budget designed
for large scale real-time model learning.
The proposed approach combines a
sparsification method based on an independence
measure with a large scale database.
In combination with an incremental learning
approach such as sequential support vector
regression, we obtain a regression method
which is applicable in real-time online learning.
It exhibits competitive learning accuracy
when compared with standard regression
techniques. Implementation on a real
robot emphasizes the applicability of the proposed
approach in real-time online model
learning for real world systems.

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Sprache(n):
 Datum: 2010-05
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: BibTex Citekey: 6505
 Art des Abschluß: -

Veranstaltung

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Titel: Thirteenth International Conference on Artificial Intelligence and Statistics (AI & Statistics 2010)
Veranstaltungsort: Chia Laguna Resort, Italy
Start-/Enddatum: 2010-05-13 - 2010-05-15

Entscheidung

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Projektinformation

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Quelle 1

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Titel: JMLR Workshop and Conference Proceedings
Genre der Quelle: Reihe
 Urheber:
Teh, YW, Herausgeber
Titterington, M, Herausgeber
Affiliations:
-
Ort, Verlag, Ausgabe: Cambridge, MA, USA : JMLR
Seiten: - Band / Heft: 9 Artikelnummer: - Start- / Endseite: 557 - 564 Identifikator: -